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The Secrets of Analytical Leaders:

Insights from Information Insiders

Wayne W. Eckerson Director of Research and Founder

Founder, BI Leadership Forum

Secrets

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Dan Ingle, Kelley Blue Book 1. Incremental development 2. Teamwork 3. One size doesn’t fit all

Amy O’Connor, Nokia 1. Data is a product 2. Create an ecosystem 3. Change management

Darren Taylor, Blue KC 1. Create the right team 2. Get executive support 3. Deliver a quick win

Eric Colson, Netflix 1. Eliminate coordination costs 2. Work fast, cohere later 3. Build with context

Tim Leonard, USXpress 1. Talk language of business 2. Let business present 3. Deliver quick wins

Kurt Thearling, CapitalOne 1. Curate the data 2. Statisticians are craftsmen 3. Manage model production

Ken Rudin, Zynga 1. Questions, not answers 2. Impacts, not insights 3. Evangelists, not oracles

Purple People

3

Straddle business and technology domains Talk the language of business Run the analytical group like a

business Recruit business people to work on

their teams Manage “front” and “back” offices

What is analytics?

Analytics with a capital “A”

Analytics with a small “a”

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Data Warehousing

Business Intelligence

Performance Management

1990s 2000s 2010 2015

“Get the data”

“Use the data”

“Improve the business”

Analytics

Desktop query/reporting Extract, transform, load tools Data warehouses

Business intelligence suites Web query/reporting On-line analytical processing (OLAP)

Packaged analytic applications Data virtualization Dashboards and scorecards

Visual discovery Operational BI Data integration suites

Cloud BI Predictive analytics Mobile BI

Hive/Pig Hadoop Text analytics

“Drive the business”

CULTURE

Data Treated as a Corporate Asset

Performance M

easurement

Fact

-bas

ed D

ecisi

ons

PEOPLE Analysts

Casual and Power Users

Data

Dev

elop

ers

ORGANIZATION Business-oriented BI Em

bedd

ed A

naly

sts

Analytical Center of Excellence

ARCHITECTURE

Bottom-up To

p-do

wn

Sandboxes

PROCESS

Cross-functional Collaboration

Deve

lopm

ent M

etho

ds Project M

anagement

DATA

Unstructured

Structured

Inte

rnal

External

Analytics success framework

• Hire people with business knowledge and emotional IQ

• Embed analysts in departments • Practice the principle of proximity • Empower teams (Scrum) or individuals

(Spanner) to build complete solutions • Foster teamwork and trust • Allow failure

Analytical Team

Autonomy, mastery, purpose

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Reporting & Monitoring (Casual Users)

Predefined Metrics

Corporate Objectives and Strategy “Business Intelligence”

Data Warehousing Architecture

Casual Users

Processes and Projects

Analysis and Prediction (Power Users)

Ad hoc queries

Analytics Architecture

Power Users

Two Types Data Developers

Analysts (embedded)

Data architects, ETL developers, report developers, data administrators, DW administrators, technical architects,

requirements specialists, trainers, etc.

Super users, business analysts, statisticians, data scientists, data analysts

TOP DOWN

BOTTOM UP

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Meet Your Analysts

David “Data

Scientist”

Beth “Business Analyst”

Sam “Super User”

Ann “Analytical Modeler”

-Answer ad hoc questions -Build reports/dashboards

-Explore data statistically & visually -Create & maintain analytical models

BUSINESS FOCUSED DATA FOCUSED

Dan “Data

Analyst”

HYBRID

-Purchase, document, and organize data

BI/Analytical Center of Excellence

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Departments BOBI Team Business team (“Business-oriented BI” team)

- Evangelize analytics - Coordinate super users and depts - Define best practices - Define and document metrics - Gather requirements - Govern reports

Sponsors Steering Committee (Executives) - Approve roadmap - Secure funding - Prioritize projects

Technical team (Data developers) - Build and maintain the EDW and Hadoop - Build semantic layer for BI tools - Create complex reports and dashboards - Develop model management platform - Coordinate databases and servers w/ IT

Super Users/ Analysts

Data governance User support

Director of BI/Analytics

• “Death march”

• Scrum

• Spanner

Deliver value fast

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Impact of specialists on development cycles

www.bileadership.com 12

Requirements Gathering

Data Modeling

ETL Development

Report Architecture

Development

Report Development

Week 1

Modeling

ETL

Report Arch

Requirements

Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8

Business-domain oriented roles

www.bileadership.com 13

ETL

Database

Reporting

Subj Area 1 Subj Area 2

ETL

Database

Reporting

Subj Area 3

ETL

Database

Reporting

• Kelley Blue Book –Intuition-driven data-driven

• Nokia –Phones data services

• Zynga –Oracles evangelists

Change Management

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Which platform do you choose?

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Structured Semi-Structured Unstructured

Hadoop

Analytic Database

General Purpose RDBMS

Reporting & Monitoring (Casual Users)

Predefined Metrics

Corporate Objectives and Strategy TOP DOWN- “Business Intelligence”

Processes and Projects

Analysis and Prediction (Power Users)

Ad hoc queries

Analysis Begets

Reports

Reports Beget

Analysis

Pros: - Alignment -Consistency Cons: - Hard to build - Politically charged - Hard to change - Expensive - “Schema Heavy”

Pros: - Quick to build - Politically uncharged - Easy to change -Low cost Cons: - Alignment - Consistency - “Schema Light”

Data Warehousing Architecture

Non-volatile Data

Analytics Architecture

Volatile Data

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BI Framework

The new analytical ecosystem

Machine Data

Web Data

Hadoop Cluster

Operational Systems(Structured data)

Power User

BI Server

Casual UserOperational System

Operational System

Documents & Text

Free-StandingSandbox

Dept Data Mart

Data Warehouse

Virtual SandboxesTop-down ArchitectureBottom-up Architecture

External Data

Audio/video Data

Streaming/ CEP Engine

Extract, Transform, Load(Batch, near real-time, or real-time)

Analytic platform or non-relational database

In-memory Sandbox

• Wayne Eckerson • weckerson@bileadership.com

Questions?

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• Analytical thought leader • Founder, BI Leadership Forum • Director of Research, TechTarget • Former director of research at TDWI • Author